---
title: "AJ's Dashboard"
output: 
  flexdashboard::flex_dashboard:
    orientation: rows
    vertical_layout: fill
    social: ["twitter","facebook","menu"]
    source_code: embed
---

```{r setup, include=FALSE}
library(flexdashboard)
library(knitr)
library(DT)
library(rpivotTable)
library(ggplot2)
library(plotly)
library(dplyr)
library(openintro)
library(highcharter)
library(ggvis)
```

```{r}
data <- read.csv("~/Desktop/VehicleFailure.csv")
```

```{r}
mycolors <- c("blue","#FFC12","darkgreen","darkorange")
```

Interactive Data Visualization
==================================

Row
-----------------------------

### car failure analysis

```{r}
valueBox(paste("Failure"),
         color = "warning")
```

### Car failure in US

```{r}
valueBox(length(data$State),
         icon = "fa-user")
```

### **Labor Cost**

```{r}
gauge(round(mean(data$lc),
            digits = 2),
            min = 0,
            max = 350,
            gaugeSectors(success = c(0,150),
                         warning = c(150,240),
                         danger = c(240,350),
                         colors = c('green','yellow','red')))
```

### Massachusetts

```{r}
valueBox(sum(data$State=="MA"),
         icon = 'fa-building')
```         

### California
```{r}
valueBox(sum(data$State=="CA"),
         icon = 'fa-building')
```

### Texas

```{r}
valueBox(sum(data$State=="TX"),
         icon = 'fa-building')
```

### Florida

```{r}
valueBox(sum(data$State=="FL"),
         icon = 'fa-building')
```

Row
----------

### Failure by State

```{r}
 p1 <- data %>%
         group_by(State) %>%
         summarise(count = n())%>%
        plot_ly(x = ~State,
                y = ~count,
                color = rainbow(51),
                type = 'bar')%>%
  layout(xaxis = list(title = "Failure By State"),
         yaxis = list(title = 'count'))
p1
```

### Top States
```{r}
p2 <- data %>%
          group_by(State)%>%
          summarise(count = n()) %>%
          filter(count > 50) %>%
          plot_ly(labels = ~State,
                  values = ~count,
                  marker = list(colors = mycolors))%>%
          add_pie(hole = 0) %>%
          layout(xaxis = list(zeroline = F,
                              showline = F,
                              showticklabels = F,
                              showgrid = F),
                 yaxis = list(zeroline = F,
                              showline = F,
                              showticklabels = F,
                              showgrid = F))
p2
```

### Failure month vs Failure Channel

```{r}
p3 <- plot_ly(data,
              x  = ~fm,
              y = ~Mileage,
              text = paste("FM;",data$fm,
                           "Mileage:",
                           data$Mileage),
              type = "bar") %>%
      layout(xaxis = list(title = "FM"),
             yaxis = list(title = "Failure Mileage"))
p3
```

Row
---------------------

### Scatter Plot of Month Vs Mileage

```{r}
p4 <- plot_ly(data,x=~fm) %>%
        add_markers(y = ~Mileage,
                    text = ~paste("Mileage: ",Mileage),
                    showlegend = F)%>%
        add_lines(y = ~fitted(loess(Mileage ~ fm)),
                  name = "Loess Smoother",
                  color = I("#FFC125"),
                  showlegend = T,
                  line = list(width = 5)) %>%
        layout(xaxis = list(title = "Month"),
               yaxis = list(title = "Mileage"))
p4
```

### Box Plot of Top State

```{r}
data %>%
        group_by(State) %>%
        ggvis(~State, ~lc, fill = ~State) %>%
        layer_boxplots()
```

Map
=============

### Map
 
```{r}
car <- data %>%
  group_by(State) %>%
  summarise(total = n())
car$State <- abbr2state(car$State)
highchart()%>%
  hc_title(text = "Car Failure in US") %>%
  hc_subtitle(text = "Source: Vehiclefailure.csv") %>%
  hc_add_series_map(usgeojson,car,
                    name = "State",
                    value = "total",
                    joinBy = c("woename","State")) %>%
  hc_mapNavigation(enabled = T)
```

Data Table
===========

```{r}
datatable(data,
          caption = "Failure Data",
          rownames = T,
          filter = "top",
          options = list(pageLength = 25))
```

Pivot Table 
======

```{r}
rpivotTable(data,
            aggregatorName = "Count",
            cols = "fm",
            rows = "State",
            rendererName = "Heatmap")
```

Summary{data-orientation=columns}
===========================================

Column {data-width = 100}
--------------

### Max Failure Month

```{r}
valueBox(max(data$fm),
         icon = "fa-user")
```

### Average Labor Cost

```{r}
valueBox(round(mean(data$lc),
               digits = 2),
         icon = "fa-area-chart")
```

### Average Mileage at Failure 

```{r}
valueBox(round(mean(data$Mileage),digits = 2),
         icon = "fa-area-chart")
```

Column
----------------------

Report

* This is a report in `r length(data$fm)` car failure. 

* The average labor cost was `r mean(data$lc)`.

* The average material cost was `r mean(data$mc)`.

This report was generated on `r format(Sys.Date(), format = "%B %d, %Y")`.

About Report
===========================

Created by: Abhishek Jaglan